Description: The XAI Framework refers to the structured approach to developing explainable AI systems that allow users to understand AI decision-making. This framework aims to address the inherent opacity of many artificial intelligence models, especially those based on deep learning, where decisions may seem like ‘black boxes’. By implementing an XAI framework, the goal is to provide transparency, allowing users not only to see the outcomes of AI decisions but also to understand the factors that influenced those decisions. This is crucial in applications where trust and accountability are essential, such as in healthcare, criminal justice, and finance. An XAI framework includes methods and tools that facilitate the interpretation of AI models, such as visualizations, example-based explanations, and confidence metrics. The relevance of this approach lies in its ability to foster the adoption of AI across various industries by ensuring that users can trust automated decisions and understand how they were reached.
History: The concept of XAI began to gain attention in the 2010s when it became clear that many AI models, especially deep learning ones, were difficult to interpret. In 2016, the U.S. Defense Advanced Research Projects Agency (DARPA) launched a research program on XAI, seeking to develop methods that would make AI systems more understandable to humans. Since then, there has been a growing academic and business interest in creating AI models that are not only accurate but also explainable.
Uses: The XAI Framework is used in various applications, including healthcare, where it is crucial to understand how an AI model arrives at a diagnosis. It is also applied in the financial sector for risk assessment and in criminal justice for decision-making regarding parole. In general, any field that relies on automated decisions can benefit from an XAI approach.
Examples: A practical example of XAI is the use of decision interpretation models in medical diagnosis systems, where doctors can see not only the diagnosis suggested by the AI but also the reasons behind that suggestion. Another example is the use of visualization tools that show how different variables influence the decisions of a credit model.
